Transient Performance Analysis of Zero-Attracting LMS

Creators: Chen, Jie; Richard, Cédric; Song, Yingying; Brie, David

Others:: Centre of Intelligent Acoustics and Immersive Com- munications at School of Marine Science and Technology, Northwestern Polytechinical University ; Department of Electrical and Computer Engineering [Univ California Davis] (ECE - UC Davis) ; University of California [Davis] (UC Davis) ; University of California (UC)-University of California (UC)-University of California [Davis] (UC Davis) ; University of California (UC)-University of California (UC); Joseph Louis LAGRANGE (LAGRANGE) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS); Centre de Recherche en Automatique de Nancy (CRAN) ; Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Description

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and 1-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its performance through comparisons with an existing model.

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Transient Performance Analysis of Zero-Attracting LMS

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